Broadly, navigation may involve identifying an entity's location and/or orientation within a frame of reference. The entity may be a person, a vehicle, an unmanned electrical or mechanical device, etc. A variety of systems for identifying an entity's location are known, such as positioning systems and inertial navigation systems. A positioning system may identify an entity's location by reference to known locations. A positioning system may facilitate detection of an entity's location by transmitting signals from a set of beacons or transmitters having known (though not necessarily fixed) locations. For example, suitable signals may be transmitted from satellites, mobile phone towers, or Wi-Fi access points. The global positioning system (GPS) is an example of a satellite-based positioning system. When four satellite locations and the corresponding distances to a GPS receiver are known, the receiver can compute its position and measure time. One of ordinary skill in the art would understand that the GPS is also an example of a global navigation satellite system (GNSS).
The signals transmitted by a positioning system may permit a suitable receiving device to detect its location via a location-detection method, such as triangulation, trilateration, multilateration, or any other method known to one of ordinary skill in the art or suitable for detecting a location of a receiving device. Triangulation is a method of identifying an entity's location relative to two known locations. Specifically, triangulation involves calculating two angles relative to a baseline through the two known locations: a first angle between the baseline and a line through the first known location and the entity, and a second angle between the baseline and a line through the second known location and the entity. The location of the entity is then calculated by treating the two known locations and the entity's location as the vertices of a triangle and applying simple geometric rules. Multilateration is a method of identifying an entity's location by measuring differences in distances between the entity's location and multiple known locations. Trilateration is similar to triangulation and multilateration, at least in the sense that trilateration, triangulation, and multilateration techniques all use information about the relationships between an entity's location and multiple known locations to pinpoint the entity's location. However, trilateration relies on measurement of distances between the entity's location and the known locations, rather than measurement of angles (as in triangulation) or measurement of differences in distances between the entity's location and the known locations (as in multilateration).
In contrast to a positioning system, an inertial navigation system (INS) estimates an entity's location based on a trusted initial location and data collected from inertial sensors (e.g., accelerometers or gyroscopes). The trusted initial location may be supplied to the INS via a positioning system. Alternatively, a trusted initial location may be supplied by any other system or method known to one of ordinary skill in the art or suitable for identifying a location of a mobile entity. For example, an aircraft may establish a trusted initial location by flying over a landmark having a known location.
After establishing a trusted initial location, the INS integrates the measurements provided by its inertial sensors to estimate the entity's velocity and position as the entity moves. Specifically, the INS collects data from the inertial sensors, uses the inertial sensor data to estimate the entity's velocity (i.e., speed and heading), and uses the estimated velocity to estimate the entity's change in location. The entity's current location is estimated to be the vector sum of the trusted initial location supplied to the INS and the change(s) in location estimated by the INS.
Errors in the INS's estimate of the entity's location may increase over time due to uncompensated errors in the INS sensor measurements. Even if the sensor measurements suffer from only small imprecisions, those imprecisions translate to small errors in the INS estimate of the entity's change in location, which accumulate in the INS estimate of the entity's current location. Accordingly, when supplied with a new trusted location for the entity, the INS may set its trusted initial location to the new trusted location, and use the discrepancy between the new trusted location and the last estimate of the entity's current location to recalibrate the inertial sensors. This process of updating and/or resetting the INS may take place (for example) periodically, at scheduled times, or when the uncertainty associated with the INS estimate of the entity's position exceeds a threshold.
A particular method or system for identifying an entity's location may be more or less accurate than another method or system, depending on the circumstances. For example, a GPS typically provides accurate location information in the absence of interference (e.g., jamming) but lacks the high rate, short-term responsiveness of an inertial navigation system. The INS is typically very responsive in the short term, but is prone to drift over time as small errors in its sensor measurements accumulate. An entity equipped with multiple systems for identifying the entity's location may rely on a navigation filter to blend the inputs of the installed systems to provide positioning estimates that are accurate and robust in the prevailing circumstances.
A navigation filter may be a weighted filter (e.g., a Kalman filter) that provides a statistically optimal estimate of an entity's position by monitoring the positioning data provided by two or more systems for identifying an entity's location and various indicators (e.g., uncertainty or confidence) of the accuracies of those location systems. For example, a GPS receiver may provide the navigation filter with an estimate of the interference associated with the GPS signals. Alternatively or additionally, a GPS receiver may provide the navigation filter with an estimate of the “uncertainty” in the GPS positioning data (e.g., an estimate of the error in the data, or an estimate of confidence in the data). The navigation filter may use the uncertainty estimate provided by the GPS receiver to weight the positioning data provided by the GPS receiver, or use the interference estimate to compute weights for the positioning data. As another example, the navigation filter may compute corrections for the positioning data provided by an inertial navigation system (INS) based on comparison of the INS outputs and the GPS outputs, and may identify error sources within the INS that are likely producing the observed accumulated error in the INS reported position. The navigation filter may then apply a filtering algorithm to the positioning data and the weights to compute a statistically optimal estimate of the entity's position.